Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score

Abstract

Since the beginning of the COVID-19 pandemic tens of millions of operations have been cancelled1 as a result of excessive postoper ative pulmonary complications (51.2 per cent) and mortality rates (23.8 per cent) in patients with perioperative SARS-CoV-2 infec tion2 . There is an urgent need to restart surgery safely in order to minimize the impact of untreated non-communicable disease. As rates of SARS-CoV-2 infection in elective surgery patients range from 1¿9 per cent3¿8 , vaccination is expected to take years to implement globally9 and preoperative screening is likely to lead to increasing numbers of SARS-CoV-2-positive patients, peri operative SARS-CoV-2 infection will remain a challenge for the foreseeable future. To inform consent and shared decision-making, a robust, globally applicable score is needed to predict individualized mor tality risk for patients with perioperative SARS-CoV-2 infection. The authors aimed to develop and validate a machine learning based risk score to predict postoperative mortality risk in patients with perioperative SARS-CoV-2 infection